A second class of explainable ML methods is based on providing post-hoc (after model training)
explanations [1]}. These methods are model-agnostic in the sense of only requiring
some training examples of the model predictions. We have recently put forward an information-theoretic
approach to computing optimal post-hoc explanations for a specific user [2]}.
